Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines
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Updated
Jun 12, 2024 - Python
Prefect is a workflow orchestration tool empowering developers to build, observe, and react to data pipelines
An open-source ML pipeline development platform
Fire up your models with the flame 🔥
A pipeline to CI/CD of a machine learning model on Google Cloud Run
Efficient streaming data ingestion, transformation & activation
Repo for running Whylogs as part of a CI workflow using github actions.
The DBT of ML, as Aligned describes data dependencies in ML systems, and reduce technical data debt
Serving large ml models independently and asynchronously via message queue and kv-storage for communication with other services [EXPERIMENT]
Find the samples, in the test data, on which your (generative) model makes mistakes.
A simple example on how to provide ML model (DecissionTreeClassifier) as a REST Service. The app is containerize and deployed in Azure Cloud
Demo usage of Weights & Biases for ML Ops
A prefect extension that builds on top of the task decorator to reduce negative engineering!
A simple Python example of a Model Service that can be fronted by the Model Sidecar
A library of computer vision models and a streamlined framework for training them.
This GitHub repository showcases the implementation of a comprehensive end-to-end MLOps pipeline using Amazon SageMaker pipelines to deploy and manage 100x machine learning models. The pipeline covers data pre-processing, model training/re-training, hyperparameter tuning, data quality check,model quality check, model registry, and model deployment.
Dicoding Submission MLOps Heart Failure Detection using ML Pipeline, Heroku Deployment and Prometheus Monitoring
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